Having laid the foundation for a theoretical understanding of attacking \(k\) NN at training time,
we turn to the question of how well our attack works in practice with our ultimate application being defense.
We explore the effectiveness of CHOPPA on synthetic data in scenarios small enough to compute the optimal one-point attack, then turn to real-world data and investigate \(\texttt {GIT}^2\texttt {ACHOPPA}\)  on larger instances.
Driven by the results on the synthetic data and with defense in mind, we look at the effect of dimensionality reduction on the attacks.
Experiments were run on 128-core machines from AWS EC2.
The QCLPs were solved using Mosek ([1]}).
Pre-processing, plotting, and data analysis were performed with pandas
([2]}), scikit-learn ([3]}) and matplotlib ([4]}).
